SCREENING FOR DISEASE

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SCREENING FOR DISEASE

• Nigel Paneth

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THREE KEY MEASURES OF VALIDITY

1. SENSITIVITY
2. SPECIFICITY
3. PREDICTIVE VALUE

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SENSITIVITY

• Sensitivity tells us how well a positive test
  detects disease.
• It is defined as the fraction of the diseased who
  test positive.
• Its complement is the false negative rate,
  defined as the fraction of the diseased who test
  negative.
• Sensitivity and false negative rate add up to
  one.

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SENSITIVITY AND THE FALSE NEGATIVE RATE ARE
COMPLEMENTARY

• N who test positive N who test negative 1
• All with disease All with disease
• SENSITIVITY FALSE NEGATIVE RATE 1

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SPECIFICITY

• Specificity tells us how well a negative test
  detects non-disease.
• It is defined as the fraction of the non-diseased
  who test negative.
• Its complement is the false positive rate,
  defined as the fraction of the non-diseased who
  test positive.
• Specificity and the false positive rate add up to
  one.

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SPECIFICITY AND THE FALSE POSITIVE RATE ARE
COMPLEMENTARY

• N who test negative N who test positive 1
• All without disease All without disease
• SPECIFICITY FALSE POSITIVE RATE 1

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DENOMINATORS OF THESE RATES

• Note that all the denominators of the four rates
  so far defined (sensitivity, specificity and the
  false and false rates) are DISEASE STATES
• The denominators of sensitivity and the false
  negative rate is PEOPLE WITH DISEASE
• The denominators of specificity and the false
  positive rate is PEOPLE WITHOUT DISEASE

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PREDICTIVE VALUE

• Positive predictive value is the proportion of
  all people with positive tests who have the
  disease.
• Negative predictive value is the proportion of
  all people with negative tests who do not have
  the disease.

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PREDICTIVE VALUES DEFINED

• POSITIVE PREDICTIVE VALUE
• All people with disease
• All people with a positive test
• NEGATIVE PREDICTIVE VALUE
• All people without disease
• All people with a negative test

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POINTS TO NOTE

• Note that the numerators and denominators are
  reversed compared to sensitivity and specificity.
  In predictive values, the denominator is the
  test result, and the numerator is disease or
  non-disease
• In general, the positive predictive value is the
  one most used. Positive predictive value and
  sensitivity are perhaps the two most important
  parameters in understanding the usefulness of a
  test under field conditions.

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CRITICAL DIFFERENCE BETWEEN DISEASE-DENOMINATORED
AND TEST-DENOMINATORED MEASURES

• Sensitivity and specificity do not vary according
  to the prevalence of the disease in the
  population.
• Predictive value of a test, however is HIGHLY
  DEPENDENT on the prevalence of the disease in the
  population

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CALCULATING THE RATES

• A test is used in 50 people with disease and 50
  people without. These are the results

Disease Disease
-
Test 48 3 51
Test - 2 47 49
50 50 100
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Disease Disease
-
Test 48 3 51
Test - 2 47 49
50 50 100

• Sensitivity 48/50 96
• Specificity 47/50 94
• Positive predictive value 48/51 94
• Negative predictive value 47/49 96

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• Now lets take this test out into a population
  where 2 of people have the disease, not 50 as
  in the previous example. Assume there are 10,000
  people, and the same sensitivity and specificity
  as before, namely 96 and 94, respectively

Disease Disease
-
Test 192 588 780
Test - 8 9,212 9,220
200 9,800 10,000
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• What is the positive predictive value now?
•  192/780 24.6
• When the prevalence of disease is 50, 94 of
  positive tests indicate disease. But when
  prevalence is only 2, less than one in four test
  results indicate a person with disease, and 2
  actually would represents a quite common disease.
  
• False positives tend to swamp true positives in
  populations, because most diseases we test for
  are rare.

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CHANGING THE THRESHOLD FOR A TEST

• When disease is defined by a threshold on a
  continuous test, the test characteristics can be
  altered by changing the threshold or cut-off
  point.
• Lowering the threshold improves sensitivity, but
  often at the price of lowered specificity (i.e.
  more false-positives).
• Raising the threshold improves specificity, but
  often at the price of lowered sensitivity (i.e.
  more false negatives).
• This can be especially important when the
  distribution of a characteristic is unimodal,
  such as blood pressure, cholesterol, weight, etc.
  (Because the gray area is so large).

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PROBLEMS WITH SCREENING

1. Do we have the right threshold?
2. Is there a truly effective treatment available
   for the discovered disease?
3. Is that treatment more effective in screened than
   non-screened cases?
4. What are the side effects of the screening
   process?
5. How efficient is screening? i.e. how many people
   must be screened to obtain a case?

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EXAMPLE OF SCREENING ASSESSMENT

• A randomized trial to assess a screening program
  for colon cancer is instituted. The intervention
  group gets regular screening, the control group
  is left to its own devices.

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• After five years it is found that
• More cases are discovered in the screened group
  than in the controls.
• The cases are discovered at an earlier stage of
  the cancer in the screened group.
• Five year survival is higher for the people with
  cancer in the screened group.
• Can we conclude that this screening program is
  necessarily effective?

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• NO, THE PROGRAM IS NOT NECESSARILY EFFECTIVE 
• The apparent benefits may only demonstrate the
  effects of LEAD-TIME BIAS. 
• If it is possible to diagnose a condition
  earlier, but not to improve survival after
  diagnosis, the screening program will have an
  over-representation of earlier diagnosed cases,
  whose survival will be increased by exactly the
  amount of time their diagnosis was advanced by
  the screening program. Thus they have not
  benefited, but the amount of time they know they
  have cancer has been increased.

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• Consider how time of diagnosis changes with
  screening in the scenario below
• unscreened group
• Dx
  Death
• Age 50 51 52 53 54 55
• screened group
• Dx
  Death
• Age 50 51 52 53 54 55

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• In the previous scenario, incidence of disease
  is initially higher, diagnosis is made earlier,
  stage of diagnosis is earlier, and duration of
  survival from diagnosis is longer. All of these
  give the impression of benefit from screening.
• However, the patient does not benefit, as death
  is not postponed.
• The only proper evidence of effectiveness of a
  screening program is a reduction of total
  age-specific mortality or morbidity, ideally
  demonstrated by randomized trial.

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MAMMOGRAPHY EXERCISEThe next two slides are
answers to questions in the following website
http//mammography.ucsf.edu/inform/index.cfm

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QUESTION 12

• Part 1. Under age 50, sensitivity is 75, over
  50, sensitivity is 90.
• Part 2. Under age 50, specificity is about 97,
  over 50, about 98.5.
• Part 3. Under age 50, PP is about 3 over 50,
  about 6-7. At all ages, about 5

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QUESTIONS 13 AND 14

• These questions raise the concept of -
• Number needed to screen
• How many women in each age group must be
  screened to save one life from breast cancer?